OpenCV tutorials--discovering The human retina and its with for image processing

Apply the discovery of human retina to image processing ~

• Spectral Whitening spectrum albino that have 3 important effects: high Spatio-temporal frequency signals canceling (noise), M Id-frequencies details enhancement and low frequencies luminance energy reduction. This directly allows visual signals cleaning of classical undesired distortions introduced by Image sensors and input luminance range.
• local logarithmic luminance brightness compression allows details to being enhanced even in low light conditions.
• decorrelation go related to the details information (parvocellular output channel) and transient information (events, MO tion made available at the Magnocellular output channel).

In the figure below, the OpenEXR image of sample Crissyfield.exr, a high Dynamic Range image is shown. In order to make it visible on the web-page, the original input image was linearly rescaled to the classical image Luminan CE range [0-255] and is converted to 8bit/channel format. Such Strong conversion hides many details because of too strong local contrasts. Furthermore, noise is also strong and pollutes visual information.

In the following image, as your retina does, local luminance adaptation, spatial noise removal and spectral whitening work Together and transmit accurate information on lower range 8bit data channels. On the noise in significantly removed, the local details hidden by strong luminance contrasts is enhanced. Output image keeps its naturalness and visual content is enhanced.

As an illustration, we apply with the following the retina model on a webcam video stream of a dark visual scene. In this visual scene, captured in an amphitheater of the university, some students is moving while talking to the teacher .

In this video sequence, because of the dark ambiance, signal to noise ratio was low and color artifacts be present on Visu Al features edges because of the low quality image capture Tool-chain.

Below is shown the retina Foveal vision applied on the entire image. In the used Retina configuration, global luminance was preserved and local contrasts are enhanced. Also, signal to noise ratio was improved:since high frequency spatio-temporal noise was reduced, enhanced details are not Corrupted by any enhanced noise.

Below is the output of the magnocellular output of the retina model. Its signals is strong where transient events occur. Here, a student are moving at the bottom of the image thus generating high energy. The remaining of the image is a static However, it's corrupted by a strong noise. Here, the retina filters out most of the noise thus generating low false motion area ' alarms '. This channel can is used as a transient/moving areas Detector:it would provide relevant information for a low cost Segme Ntation tool that would highlight areas in which a event is occurring.

OpenCV tutorials--discovering The human retina and its with for image processing